An Efficient Method for High-Speed Railway Dropper Fault Detection Based on Depthwise Separable Convolution

Abstract

The overhead contact system (OCS) is an indispensable part of high-speed railway power supply system. The stable operation of the OCS depends on the stability of the contact between the pantograph and catenary. The dropper is an important guarantee for the stable contact. The three faults of the dropper, such as slack, breakage and disappearance can damage the quality of traction power supply and reduce the safety of the railway operation. Therefore, it is necessary to efficiently detect the dropper fault and guide the maintenance in time. Recently, automatic dropper fault detection methods based on monitor-video have been introduced to improve railway operation safety. However, the existing methods were still not stable enough in complex backgrounds. To improve the accuracy and real-time performance of the dropper fault detection, this paper proposed a method combining depthwise separable convolution with object detection network to detect the dropper fault. The proposed method consists of two stages. First, a dropper progressive location network (DPLN) was adopted to obtain the dropper. The DPLN was mainly composed of a pantograph location network (PLN) and a dropper location network (DLN). Then, the dropper fault recognition network (DFRN) was used to recognize the type of dropper fault. The experimental results demonstrated the accuracy and real-time performance of the proposed method.